Satellite synthetic aperture radar (SAR) interferometry (InSAR) is a powerful technology to monitor slow ground surface movements. However, the extraction and interpretation of information from big sets of InSAR measurements is a complex and demanding task. In this paper, a new method is presented for automatically detecting potential instability risks affecting buildings and infrastructures, by searching for anomalies in the persistent scatterer (PS) deformations, either in the spatial or in the temporal dimensions. In the spatial dimension, in order to reduce the dataset size and improve data reliability, we utilize a hierarchical clustering method to obtain convergence points that are more trustworthy. Then, we detect deformations characterized by large values and spatial inhomogeneity. In the temporal dimension, we use a signal processing method to decompose the input into two main components: regular periodic deformations and piecewise linear deformations. After removing the periodic component, the velocity variation in each identified temporal partition is analyzed to detect anomalous velocity trends and accelerations. The method has been tested on different sites in China, based on InSAR measurements from COSMO-SkyMed data. The results, verified with in-field surveys, confirm the potential of the method for the automatic detection of deformation anomalies that could cause building or infrastructure stability problems.

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The authors first review the recently proposed Das's biometric-based remote user authentication scheme, and then show that Das's scheme is still insecure against some attacks and has some problems in password change phase. In order to overcome the design flaws in Das's scheme, an improvement of the scheme is further proposed. Cryptanalysis shows that our scheme is more efficient and secure against most of attacks; moreover, our scheme can provide strong mutual authentication by using verifying biometric, password as well as random nonces generated by the user and server.

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